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getomni-ai
zerox
TypeScript2024–2026
getomni-ai / zerox

zerox

OCR & Document Extraction using vision models

ProductivityAI & Machine Learning
Explore on GitHubRead Hacker News discussions
7Distinct people
17Mentions
0Explicit recommenders
12.3KGitHub stars
REPOSITORY AT A GLANCE

What to know before exploring

What it is

OCR & Document Extraction using vision models

GitHub

Where it is used

No documented product use has been verified yet.

What people are saying

No recommendation phrase was found in the indexed comments.

No classified warning is attached to this page. Read the discussions before treating silence as approval.

Also in these discussions

Mentioned in the same Hacker News threads. That is not proof they are substitutes.

This week on Hacker News

Nov 7–Nov 14 UTC · Direct repository links and separately labeled related project stories. Indexed through 11/14/2026, 5:04:57 PM UTC.

No direct repository links were found in the indexed HN discussions over the past seven days. Older discussions are available below.

Mentions over time

HN mentions by month17 dated mentions

Observed mentions: 2024-07-23 16:49:53 UTC to 2026-03-02 19:31:18 UTC.

0123452024-07-01 00:00:00 UTC to 2024-08-01 00:00:00 UTC (exclusive): 1 mentions; 1 submissions, 0 commentsJul 20242024-08-01 00:00:00 UTC to 2024-09-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 comments2024-09-01 00:00:00 UTC to 2024-10-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 comments2024-10-01 00:00:00 UTC to 2024-11-01 00:00:00 UTC (exclusive): 1 mentions; 1 submissions, 0 comments2024-11-01 00:00:00 UTC to 2024-12-01 00:00:00 UTC (exclusive): 5 mentions; 1 submissions, 4 comments2024-12-01 00:00:00 UTC to 2025-01-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2025-01-01 00:00:00 UTC to 2025-02-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 comments2025-02-01 00:00:00 UTC to 2025-03-01 00:00:00 UTC (exclusive): 3 mentions; 1 submissions, 2 comments2025-03-01 00:00:00 UTC to 2025-04-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2025-04-01 00:00:00 UTC to 2025-05-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2025-05-01 00:00:00 UTC to 2025-06-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 commentsMay 20252025-06-01 00:00:00 UTC to 2025-07-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2025-07-01 00:00:00 UTC to 2025-08-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2025-08-01 00:00:00 UTC to 2025-09-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2025-09-01 00:00:00 UTC to 2025-10-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2025-10-01 00:00:00 UTC to 2025-11-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2025-11-01 00:00:00 UTC to 2025-12-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2025-12-01 00:00:00 UTC to 2026-01-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 comments2026-01-01 00:00:00 UTC to 2026-02-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 comments2026-02-01 00:00:00 UTC to 2026-03-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 comments2026-03-01 00:00:00 UTC to 2026-04-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 commentsMar 2026

Counts come from the repository’s dated source evidence. Index cutoff: 2026-11-02 08:16:59 UTC.

View mention counts and date ranges
UTC ranges include their start and exclude their end. Select a period to filter the source evidence.
PeriodStart (UTC)End (exclusive, UTC)MentionsSubmissionsComments
Jul 20242024-07-01 00:00:00 UTC2024-08-01 00:00:00 UTC110
Aug 20242024-08-01 00:00:00 UTC2024-09-01 00:00:00 UTC101
Sep 20242024-09-01 00:00:00 UTC2024-10-01 00:00:00 UTC101
Oct 20242024-10-01 00:00:00 UTC2024-11-01 00:00:00 UTC110
Nov 20242024-11-01 00:00:00 UTC2024-12-01 00:00:00 UTC514
Dec 20242024-12-01 00:00:00 UTC2025-01-01 00:00:00 UTC000
Jan 20252025-01-01 00:00:00 UTC2025-02-01 00:00:00 UTC101
Feb 20252025-02-01 00:00:00 UTC2025-03-01 00:00:00 UTC312
Mar 20252025-03-01 00:00:00 UTC2025-04-01 00:00:00 UTC000
Apr 20252025-04-01 00:00:00 UTC2025-05-01 00:00:00 UTC000
May 20252025-05-01 00:00:00 UTC2025-06-01 00:00:00 UTC000
Jun 20252025-06-01 00:00:00 UTC2025-07-01 00:00:00 UTC000
Jul 20252025-07-01 00:00:00 UTC2025-08-01 00:00:00 UTC000
Aug 20252025-08-01 00:00:00 UTC2025-09-01 00:00:00 UTC000
Sep 20252025-09-01 00:00:00 UTC2025-10-01 00:00:00 UTC000
Oct 20252025-10-01 00:00:00 UTC2025-11-01 00:00:00 UTC000
Nov 20252025-11-01 00:00:00 UTC2025-12-01 00:00:00 UTC000
Dec 20252025-12-01 00:00:00 UTC2026-01-01 00:00:00 UTC101
Jan 20262026-01-01 00:00:00 UTC2026-02-01 00:00:00 UTC101
Feb 20262026-02-01 00:00:00 UTC2026-03-01 00:00:00 UTC101
Mar 20262026-03-01 00:00:00 UTC2026-04-01 00:00:00 UTC101

What Hacker News says

17 sources
themanmaran·Mar 2, 2026
OmniAI (YC W24) | https://getomni.ai | Full Stack Engineers | SF In-Person | Full Time | $125k–$200k • 0.50%–1.50% Tech stack: TypeScript | Node | React/Next.js | Postgres | Docker | K8s | LLMs We’re building the AI-powered infrastructure layer for small-business lending. Banks and fintechs use Omni to automate document collection, financial modeling, public-record research, and ongoing borrower communication. You’ll work on: 1. Wrangling messy PDFs into usable data (we built a major open-source extraction library: https://github.com/getomni-ai/zerox) 2. Aggregating millions of datapoints from 50+ public sources (UCCs, liens, licenses, tax records) + credit/KYC/AML data 3. Building real-time financial models that combine OCR, LLMs, and rule-based systems into predictable outputs We’re early, you’ll have a huge impact. Email me (my name at
Read the rest
the URL) or apply here: https://www.ycombinator.com/companies/omniai/jobs/Dr0GIaE-fu...
In Ask HN: Who is hiring? (March 2026)
themanmaran·Feb 2, 2026
OmniAI (YC W24) | https://getomni.ai | Full Stack Engineers | SF In-Person | Full Time | $125k–$175k • 0.50%–1.50% Tech stack: TypeScript | Node | React/Next.js | Postgres | Docker | K8s | LLMs We’re building the AI-powered infrastructure layer for small-business lending. Banks and fintechs use Omni to automate document collection, financial modeling, public-record research, and ongoing borrower communication. You’ll work on: 1. Wrangling messy PDFs into usable data (we built a major open-source extraction library: https://github.com/getomni-ai/zerox) 2. Aggregating millions of datapoints from 50+ public sources (UCCs, liens, licenses, tax records) + credit/KYC/AML data 3. Building real-time financial models that combine OCR, LLMs, and rule-based systems into predictable outputs We’re early, you’ll have a huge impact. Email me (my name at
Read the rest
the URL) or apply here: https://www.ycombinator.com/companies/omniai/jobs/Dr0GIaE-fu...
In Ask HN: Who is hiring? (February 2026)
themanmaran·Jan 3, 2026
OmniAI (YC W24) | https://getomni.ai | Full Stack Engineers | SF In-Person | Full Time | $125k–$175k • 0.50%–1.50% Tech stack: TypeScript | Node | React/Next.js | Postgres | Docker | K8s | LLMs We’re building the AI-powered infrastructure layer for small-business lending. Banks and fintechs use Omni to automate document collection, financial modeling, public-record research, and ongoing borrower communication. You’ll work on: 1. Wrangling messy PDFs into usable data (we built a major open-source extraction library: https://github.com/getomni-ai/zerox) 2. Aggregating millions of datapoints from 50+ public sources (UCCs, liens, licenses, tax records) + credit/KYC/AML data 3. Building real-time financial models that combine OCR, LLMs, and rule-based systems into predictable outputs We’re early, you’ll have a huge impact. Email me (my name at
Read the rest
the URL) or apply here: https://www.ycombinator.com/companies/omniai/jobs/Dr0GIaE-fu...
In Ask HN: Who is hiring? (January 2026)
themanmaran·Dec 1, 2025
OmniAI (YC W24) | https://getomni.ai | Full Stack Engineers | SF In-Person | Full Time | $125k–$175k • 0.50%–1.50% Tech stack: TypeScript | Node | React/Next.js | Postgres | Docker | K8s | LLMs We’re building the AI-powered infrastructure layer for small-business lending. Banks and fintechs use Omni to automate document collection, financial modeling, public-record research, and ongoing borrower communication. You’ll work on: 1. Wrangling messy PDFs into usable data (we built a major open-source extraction library: https://github.com/getomni-ai/zerox) 2. Aggregating millions of datapoints from 50+ public sources (UCCs, liens, licenses, tax records) + credit/KYC/AML data 3. Building real-time financial models that combine OCR, LLMs, and rule-based systems into predictable outputs We’re early, you’ll have a huge impact. Email me (my name at
Read the rest
the URL) or apply here: https://www.ycombinator.com/companies/omniai/jobs/Dr0GIaE-fu...
In Ask HN: Who is hiring? (December 2025)
themanmaran·Feb 20, 2025Submission

Show HN: Benchmarking VLMs vs. Traditional OCR

Vision models have been gaining popularity as a replacement for traditional OCR. Especially with Gemini 2.0 becoming cost competitive with the cloud platforms. We've been continuously evaluating different models since we released the Zerox package last year (https://github.com/getomni-ai/zerox). And we wanted to put some numbers behind it. So we’re open sourcing our internal OCR benchmark + evaluation datasets. Full writeup + data explorer here: https://getomni.ai/ocr-benchmark Github: https://github.com/getomni-ai/benchmark Huggingface: https://huggingface.co/datasets/getomni-ai/ocr-benchmark Couple notes on the methodology: 1. We are using JSON accuracy as our primary metric. The end goal is to evaluate how well each OCR provider can prepare the data for LLM ingestion. 2. This methodology differs from a lot of OCR benchmarks, because it
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doesn't rely on text similarity. We believe text similarity measurements are heavily biased towards the exact layout of the ground truth text, and penalize correct OCR that has slight layout differences. 3. Every document goes Image => OCR => Predicted JSON. And we compare the predicted JSON against the annotated ground truth JSON. The VLMs are capable of Image => JSON directly, we are primarily trying to measure OCR accuracy here. Planning to release a separate report on direct JSON accuracy next week. This is a continuous work in progress! There are at least 10 additional providers we plan to add to the list. The next big roadmap items are: - Comparing OCR vs. direct extraction. Early results here show a slight accuracy improvement, but it’s highly variable on page length. - A multilingual comparison. Right now the evaluation data is english only. - A breakdown of the data by type (best model for handwriting, tables, charts, photos, etc.)
146 points · Open discussion
themanmaran·Jan 2, 2025
OmniAI (YC W24) | https://getomni.ai | Full stack Engineers | SF In-Person | Full Time | $125k – $175k • 0.50% – 01.50% Tech stack is TypeScript | Postgres | Docker | React | LLM Help us build the best OCR / document extraction on the planet! You can check out our open source library [https://github.com/getomni-ai/zerox]. Or try out our in house model [https://getomni.ai/ocr-demo]. The main things we spend our time on: 1. Wrangling LLMs into providing predictable outputs 2. Running document extractions at scale 3. Building training data for vision models If you're interested going really deep on the terrible world of PDFs, I'd love to chat! You drop me an email (my name at the url) or apply at https://www.workatastartup.com/jobs/67540
In Ask HN: Who is hiring? (January 2025)
ankenyr·Nov 22, 2024
Your initial commit makes it look like you wrote all the code. https://github.com/DocumindHQ/documind/commit/d91121739df038... This is because you copied and uploaded the code instead of forking. You could do a lot by restoring attribution. Your history would look the same as https://github.com/getomni-ai/zerox/commits/main/ and diverge from where you forked. People are getting upset because this is not a nice thing to do. Attribution is significant. No one would care if you replaced all the names with the new ones in a fork because they would see commits that do that.
In Show HN: Documind – Open-source AI tool to turn documents into structured data
themanmaran·Nov 18, 2024Submission

Documind ripped our open source tool and swapped the license

Saw a ShowHN post [0] today about an open source document extractor tool. I thought the workflow sounded pretty similar to our library, and only to realize it's a direct rip of Zerox [1]. Looking through the code: 1. They removed the MIT License and added AGPL. 2. It's not a fork, they purposefully copied the code and did a find/replace to swap out Zerox for their name. 3. There is no attribution or mention of the original library. 4. They are trying to market this as a competitive product. If you inspect the source code, it's a verbatim copy. They literally just renamed the `ZeroxOutput` to `DocumindOutput` [2][3]. I recognize that plenty of people are using Zerox within commercial products, but to copy to copy the code and pitch it as your own open source product is pretty fraudulent. Especially slapping a copyleft license on it. [0] ht
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tps://news.ycombinator.com/item?id=42171311 [1] https://github.com/getomni-ai/zerox [2] https://github.com/DocumindHQ/documind/blob/main/core/src/types.ts#L25 [3] https://github.com/getomni-ai/zerox/blob/main/node-zerox/src/types.ts#L35
32 points · Open discussion
themanmaran·Nov 18, 2024
Disappointed to see this is an exact rip of our open source tool zerox [1]. With no attribution. They also took the MIT License and changed it out for an AGPL. If you inspect the source code, it's a verbatim copy. They literally just renamed the ZeroxOutput to DocumindOutput [2][3] [1] https://github.com/getomni-ai/zerox [2] https://github.com/DocumindHQ/documind/blob/main/core/src/ty... [3] https://github.com/getomni-ai/zerox/blob/main/node-zerox/src...
In Show HN: Documind – Open-source AI tool to turn documents into structured data
bbor·Nov 16, 2024
Looks awesome! Been doing a lot of OCR recently, and love the addition to the space. The reigning champion in the PDF -> Markdown space (AFAIK) is Facebook's Nougat[1], and I'm excited to hook this up to DSPy and see which works better for philosophy books. This repo links the Zerox[2] project by some startup, which also looks awesome, and certainly more smoothly advertised than Nougat. Would love corrections/advice from any actual experts passing by this comment section :) That said, I have a few questions if OP/anyone knows the answers: 1. What is Together.ai, and is this model OSS? Their website sells them as a hosting service, and the "Custom Models" page[3] seems to be about custom finetuning, not, like, training new proprietary models in-house. They might have a HuggingFace profile but it's hard to tell if it's them https://huggingf
Read the rest
ace.co/TogetherAI 2. The GitHub says "hosted demo", but the hosting part is just the tiny (clean!) WebGUI, yes? It's implied that this functionality is and will always be available only through API calls? P.S. The header links are broken on my desktop browser -- no onClick triggered [1] https://facebookresearch.github.io/nougat/ [2] https://github.com/getomni-ai/zerox [3] https://www.together.ai/products#custom-models
In Llama-OCR: Document to Markdown
themanmaran·Oct 17, 2024Submission

Show HN: Zerox v1 – Document OCR with GPT-vision

Hey everyone! Today we're launching the stable release of Zerox, our open source OCR tool we've been building at OmniAi. This started out as a weekend hack with gpt-4-mini, using the very basic strategy of "just ask the ai to ocr the document". But this turned out to be better performing than our current implementation of Unstructured/Textract. At pretty much the same cost. In particular, we've seen the vision models do a great job on charts, infographics, and handwritten text. Documents are a visual format after all, so a vision model makes sense! I posted the first experiments on HN, and since then, we've had some great contributors who have helped turn this into a full package. We have two versions now: - pip package [https://pypi.org/project/py-zerox/] - npm package [https://www.npmjs.com/package/zerox] Next steps for us are working o
Read the rest
n building an open source dataset for fine tuning. We've seen some early success with a charts=>markdown fine tuning data set, and excited to keep building. Github: https://github.com/getomni-ai/zerox You can try out a hosted version here: https://getomni.ai/ocr-demo
7 points · Open discussion

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